Abstract
We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users' plans and goals. The application domain is a Multi-User Dungeon adventure game with thousands of possible actions and locations. We propose several network structures which represent the relations in the domain to varying extents, and compare their predictive power for predicting a user's current goal, next action and next location. The conditional probability distributions for each network are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple abstraction and learning techniques in order to speed up the performance of the most promising dynamic belief networks without a significant change in the accuracy of goal predictions. Our experimental results in the application domain show a high degree of predictive accuracy. This indicates that dynamic belief networks in general show promise for predicting a variety of behaviours in domains which have similar features to those of our domain, while reduced models, obtained by means of learning and abstraction, show promise for efficient goal prediction in such domains.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Albrecht, D. W., Zukerman, I. and Nicholson, A. E.: 1997, Bayesian Models for Keyhole Plan Recognition in an Adventure Game (Extended Version). Technical Report 328, Department of Computer Science, Monash University, Victoria, Australia.
Allen, J. and Perrault, C.: 1980, Analyzing intention in utterances. Artificial Intelligence 15, 143-178.
Balabanovi´c, M.: 1998, Exploring versus exploiting when learning user models for text recommendation. User Modeling and User-adapted Interaction, this issue.
Bauer, M.: 1996, Acquisition of user preferences for plan recognition. In UM96 - Proceedings of the Fifth International Conference on User Modeling, 105-112, Kona, Hawaii.
Box, G. E. and Tiao, G. C.: 1973, Bayesian Inference in Statistical Analysis. Addison-Wesley Publishing Company, Philippines.
Buntine, W.: 1996, A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8(2), 195-210.
Cañamero, D., Delannoy, J., and Kodratoff, Y.: 1992, Building explanations in a plan recognition system for decision support. In ECAI92 Workshop on Improving the Use of Knowledge-Based Systems with Explanations, 35-45, Vienna, Austria.
Carberry, S.: 1990, Incorporating default inferences into plan recognition. In AAAI90 - Proceedings of the Eight National Conference on Artificial Intelligence, 471-478, Boston, Massachusetts.
Charniak, E.: 1993, Statistical Language Learning. MIT Press, Cambridge, Massachusetts.
Charniak, E.: 1997, Personal communication.
Charniak, E. and Goldman, R. P.: 1993, A Bayesian model of plan recognition. Artificial Intelligence 64(1), 50-56.
Conati, C., Gertner, A. S., VanLehn, K., and Druzdzel, M.: 1997, On-line student modeling for coached problem solving using Bayesian Networks. In UM97 - Proceedings of the Sixth International Conference on User Modeling, 231-242, Sardinia, Italy.
Dagum, P., Galper, A., and Horvitz, E.: 1992, Dynamic network models for forecasting. In UAI92 - Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, 41-48, Stanford, California.
Dean, T. and Wellman, M. P.: 1991, Planning and control. Morgan Kaufmann Publishers, San Mateo, California.
Forbes, J., Huang, T., Kanazawa, K., and Russell, S.: 1995, The BATmobile: Towards a Bayesian automated taxi. In IJCAI95 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1878-1885, Montreal, Canada.
Good, I. J. 1965, The Estimation of Probabilities: An Essay on Modern Bayesian Methods. Research Monograph No. 30. MIT Press, Cambridge, Massachusetts.
Heckerman, D.: 1995, A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research.
Huber, M. J., Durfee, E. H. and Wellman, M. P.: 1994, The automated mapping of plans for plan recognition. In UAI94 - Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 344-350, Seattle, Washington.
Jameson, A.: 1996, Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction 5, 193-251.
Joachims, T., Freitag, D. and Mitchell, T.: 1997, Webwatcher: A tour guide for the world wide web. In IJCAI97 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 770-775, Nagoya, Japan.
Lesh, N.: 1997, Adaptive goal recognition. In IJCAI97 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1208-1214, Nagoya, Japan.
Lesh, N. and Etzioni, O.: 1995, A sound and fast goal recognizer. In IJCAI95 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1704-1710, Montreal, Canada.
Lesh, N. and Etzioni, O.: 1996, Scaling up goal recognition. In Principles of Knowledge Representation and Reasoning, 178-189.
Litman, D. and Allen, J. F.: 1987, A plan recognition model for subdialogues in conversation. Cognitive Science 11, 163-200.
Moukas, A. and Maes, P.: 1998, User modeling in an evolving multi-agent system. Submitted.
Nicholson, A. E. and Brady, J. M.: 1994, Dynamic belief networks for discrete monitoring. IEEE Systems, Man and Cybernetics 24(11), 1593-1610.
Pearl, J.: 1988, Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann Publishers, San Mateo, California.
Pynadath, D. and Wellman, M.: 1995, Accounting for context in plan recognition with application to traffic monitoring. In UAI95 - Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 472-481, Montreal, Canada.
Quinlan, J. R.: 1983, Learning efficient classification procedures and their application to chess end games. In Michalski, R. S. and Carbonell, J., editors, Machine Learning: an Artificial Intelligence Approach. Tioga Publishing Company, Palo Alto, California.
Raskutti, B.: 1993, Handling Uncertainty during Plan Recognition for Response Generation. PhD thesis, Monash University, Victoria, Australia.
Raskutti, B. and Zukerman, I.: 1991, Generation and selection of likely interpretations during plan recognition. User Modeling and User Adapted Interaction 1(4), 323-353.
Russell, S., Binder, J., Koller, D. and Kanazawa, K.: 1995, Local learning in probabilistic networks with hidden variables. In IJCAI95 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1146-1152, Montreal, Canada.
Waern, A. and Stenborg, O.: 1995, Recognizing the plans of a replanning user. In Proceedings of the IJCAI-95 Workshop on The Next Generation of Plan Recognition Systems: Challenges for and Insight from Related Areas of AI, 113-118, Montreal, Canada.
Wallace, C.: 1990, Classification by minimum-message-length inference. In Goos, G. and Hartmanis, J., editors, ICCI' 90 - Advances in Computing and Information, 72-81. Springer-Verlag, Berlin.
Wallace, C. and Boulton, D.: 1968, An information measure for classification. The Computer Journal 11, 185-194.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Albrecht, D.W., Zukerman, I. & Nicholson, A.E. Bayesian Models for Keyhole Plan Recognition in an Adventure Game. User Modeling and User-Adapted Interaction 8, 5–47 (1998). https://doi.org/10.1023/A:1008238218679
Issue Date:
DOI: https://doi.org/10.1023/A:1008238218679